Saliva-based detection of COVID-19 infection in a real-world setting using reagent-free Raman spectroscopy and machine learning

  1. Katherine Ember
  2. François Daoust
  3. Myriam Mahfoud
  4. Frédérick Dallaire
  5. Esmat Zamani
  6. Trang Tran
  7. Arthur Plante
  8. Mame-Kany Diop
  9. Tien Nguyen
  10. Amélie St-Georges-Robillard
  11. Nassim Ksantini
  12. Julie Lanthier
  13. Antoine Filiatrault
  14. Guillaume Sheehy
  15. Caroline Quach
  16. Dominique Trudel
  17. Frédéric Leblond
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Abstract

Significance

The primary method of COVID-19 detection is reverse transcription polymerase chain reaction (RT-PCR) testing. PCR test sensitivity may decrease as more variants of concern arise.

Aim

We aimed to develop a reagent-free way to detect COVID-19 in a real-world setting with minimal constraints on sample acquisition.

Approach

We present a workflow for collecting, preparing and imaging dried saliva supernatant droplets using a non-invasive, label-free technique – Raman spectroscopy – to detect changes in the molecular profile of saliva associated with COVID-19 infection.

Results

Using machine learning and droplet segmentation, amongst all confounding factors, we discriminated between COVID-positive and negative individuals yielding receiver operating coefficient (ROC) curves with an area under curve (AUC) of 0.8 in both males (79% sensitivity, 75% specificity) and females (84% sensitivity, 64% specificity). Taking the sex of the saliva donor into account increased the AUC by 5%.

Conclusion

These findings may pave the way for new rapid Raman spectroscopic screening tools for COVID-19 and other infectious diseases.

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